Abstract
This paper introduces the segmentation of Neisseria bacterial meningitis images. Images segmentation is an operation of identifying the homogeneous location in a digital image. The basic idea behind segmentation called thresholding, which be classified as single thresholding and multiple thresholding. To perform images segmentation, transformations and morphological operations processes are used to segment the images, as well as image transformation an edge detecting, filling operation, design structure element, and arithmetic operations technique is used to implement images segmentation. The images segmentation represent significant step in extracting images features and diagnoses the disease by computer software applications.
Highlights
In computer vision techniques, segmentation is the process of partitioning a digital image into multiple segments sets of pixels, known as super pixel[1]
This paper introduces the segmentation of Neisseria bacterial meningitis images
This is a segmentation algorithm designed to extract a single bacterial cell from the other Cerebrospinal fluid (CSF) components shown in the image and separate the bacteria region from the CSF region
Summary
Segmentation is the process of partitioning a digital image into multiple segments sets of pixels, known as super pixel[1]. The goal of segmentation is to simplify or change the representation of an image into something that is more meaningful and easier to analyze. Image fragmentation algorithms generally based on one of two basic properties of density values: discontinuity and similarity[3]. The approach is to divide Image segmentation algorithms generally based on one of two basic properties of intensity values: discontinuity and similarity[4]. The approach is to partition an image based on abrupt changes in intensity, an edge-detection algorithms fall in this category. An image partitioned into regions that are similar according to set of defined criteria[5]. Many biological images contain of light objects over a constant dark background (especially those obtained using fluorescence microscopy); in such a way that object and background pixels have gray levels grouped into two dominant modes[6]
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